Revolutionizing Postoperative Ileus Monitoring: Exploring GRU-D's Real-Time Capabilities and Cross-Hospital Transferability.

Background: Postoperative ileus (POI) after colorectal surgery leads to increased morbidity, costs, and hospital stays. Identifying POI risk for early intervention is important for improving surgical outcomes especially given the increasing trend towards early discharge after surgery. While existing studies have assessed POI risk with regression models, the role of deep learning's remains unexplored. Methods: We assessed the performance and transferability (brutal force/instance/parameter transfer) of Gated Recurrent Unit with Decay (GRU-D), a longitudinal deep learning architecture, for real-time risk assessment of POI among 7,349 colorectal surgeries performed across three hospital sites operated by Mayo Clinic with two electronic health records (EHR) systems. The results were compared with atemporal models on a panel of benchmark metrics. Results: GRU-D exhibits robust transferability across different EHR systems and hospital sites, showing enhanced performance by integrating new measurements, even amid the extreme sparsity of real-world longitudinal data. On average, for labs, vitals, and assisted living status, 72.2%, 26.9%, and 49.3% respectively lack measurements within 24 hours after surgery. Over the follow-up period with 4-hour intervals, 98.7%, 84%, and 95.8% of data points are missing, respectively. A maximum of 5% decrease in AUROC was observed in brutal-force transfer between different EHR systems with non-overlapping surgery date frames. Multi-source instance transfer witnessed the best performance, with a maximum of 2.6% improvement in AUROC over local learning. The significant benefit, however, lies in the reduction of variance (a maximum of 86% decrease). The GRU-D model's performance mainly depends on the prediction task's difficulty, especially the case prevalence rate. Whereas the impact of training data and transfer strategy is less crucial, underscoring the challenge of effectively leveraging transfer learning for rare outcomes. While atemporal Logit models show notably superior performance at certain pre-surgical points, their performance fluctuate significantly and generally underperform GRU-D in post-surgical hours. Conclusion: GRU-D demonstrated robust transferability across EHR systems and hospital sites with highly sparse real-world EHR data. Further research on built-in explainability for meaningful intervention would be highly valuable for its integration into clinical practice.

Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial.

BACKGROUND: As primary care populations age, timely identification of palliative care need is becoming increasingly relevant. Previous studies have targeted particular patient populations with life-limiting disease, but few have focused on patients in a primary care setting. Toward this end, we propose a stepped-wedge pragmatic randomized trial whereby a machine learning algorithm identifies patients empaneled to primary care units at Mayo Clinic (Rochester, Minnesota, United States) with high likelihood of palliative care need. METHODS: 42 care team units in 9 clusters were randomized to 7 wedges, each lasting 42 days. For care teams in treatment wedges, palliative care specialists review identified patients, making recommendations to primary care providers when appropriate. Care teams in control wedges receive palliative care under the standard of care. DISCUSSION: This pragmatic trial therefore integrates machine learning into clinical decision making, instead of simply reporting theoretical predictive performance. Such integration has the possibility to decrease time to palliative care, improving patient quality of life and symptom burden. TRIAL REGISTRATION: Clinicaltrials.gov NCT04604457 , restrospectively registered 10/26/2020. PROTOCOL: v0.5, dated 9/23/2020.

Effect of an Artificial Intelligence Decision Support Tool on Palliative Care Referral in Hospitalized Patients: A Randomized Clinical Trial.

CONTEXT: Palliative care services are commonly provided to hospitalized patients, but accurately predicting who needs them remains a challenge. OBJECTIVES: To assess the effectiveness on clinical outcomes of an artificial intelligence (AI)/machine learning (ML) decision support tool for predicting patient need for palliative care services in the hospital. METHODS: The study design was a pragmatic, cluster-randomized, stepped-wedge clinical trial in 12 nursing units at two hospitals over a 15-month period between August 19, 2019, and November 17, 2020. Eligible patients were randomly assigned to either a medical service consultation recommendation triggered by an AI/ML tool predicting the need for palliative care services or usual care. The primary outcome was palliative care consultation note. Secondary outcomes included: hospital readmissions, length of stay, transfer to intensive care and palliative care consultation note by unit. RESULTS: A total of 3183 patient hospitalizations were enrolled. Of eligible patients, A total of 2544 patients were randomized to the decision support tool (1212; 48%) and usual care (1332; 52%). Of these, 1717 patients (67%) were retained for analyses. Patients randomized to the intervention had a statistically significant higher incidence rate of palliative care consultation compared to the control group (IRR, 1.44 [95% CI, 1.11-1.92]). Exploratory evidence suggested that the decision support tool group reduced 60-day and 90-day hospital readmissions (OR, 0.75 [95% CI, 0.57, 0.97]) and (OR, 0.72 [95% CI, 0.55-0.93]) respectively. CONCLUSION: A decision support tool integrated into palliative care practice and leveraging AI/ML demonstrated an increased palliative care consultation rate among hospitalized patients and reductions in hospitalizations.

Real-time risk prediction of colorectal surgery-related post-surgical complications using GRU-D model.

BACKGROUND: Post-surgical complications (PSCs) have been an increasing concern for hospitals in light of Medicare penalties for 30-day readmissions. PSCs have become a target for quality improvement and benchmark for the healthcare system. Over half (60 %) of the deep or organ space surgical site infections are discovered after discharge, leading to a readmission. There has thus been a push to develop risk prediction models for targeted preventive interventions for PSCs. OBJECTIVE: We experiment several Gated Recurrent Unit with Decay (GRU-D) based deep learning architectures with various feature sampling schemes in predicting the risk of colorectal PSCs and compare with atemporal logistic regression models (logit). METHOD: We used electronic health record (EHR) data of 3,535 colorectal surgical patients involved in the national surgical quality improvement program (NSQIP) between 2006 and 2018. Single layer, stacked layer, and multimodal GRU-D models with sigmoid activation were used to develop risk prediction models. Area Under the Receiver Operating Characteristic curve (AUROC) was calculated by comparing predicted probability of developing complications versus truly observed PSCs (NSQIP adjudicated) within 30 days after surgery. We set up cross-validation and an independent held-out dataset for testing model performance consistency. RESULTS AND CONCLUSION: The primary contribution of our study is the formulation of a novel real-time PSC risk prediction task using GRU-D with demonstrated clinical utility. GRU-D outperforms the logit model in predicting wound and organ space infection and shows improved performance as additional data points become available. Logit model outperforms GRU-D before surgery for superficial infection and bleeding. For the same sampling scheme, there is no obvious advantage of complex architectures (stacked, multimodal) over single layer GRU-D. Obtaining more data points closer to the occurrence of PSCs is more important than using a more frequent sampling scheme in training GRU-D models. The fourth predicted risk quartile by single layer GRU-D contains 63 %, 59 %, and 66 % organ space infection cases, at 4 h before, 72 h after, and 168 h after surgery, respectively, suggesting its potential application as a bedside risk assessment tool.

Assessment of Data Quality Variability across Two EHR Systems through a Case Study of Post-Surgical Complications.

Translation of predictive modeling algorithms into routine clinical care workflows faces challenges in the form of varying data quality-related issues caused by the heterogeneity of electronic health record (EHR) systems. To better understand these issues, we retrospectively assessed and compared the variability of data produced from two different EHR systems. We considered three dimensions of data quality in the context of EHR-based predictive modeling for three distinct translational stages: model development (data completeness), model deployment (data variability), and model implementation (data timeliness). The case study was conducted based on predicting post-surgical complications using both structured and unstructured data. Our study discovered a consistent level of data completeness, a high syntactic, and moderate-high semantic variability across two EHR systems, for which the quality of data is context-specific and closely related to the documentation workflow and the functionality of individual EHR systems.

Risk Prediction Model for 6-Month Mortality for Patients Discharged to Skilled Nursing Facilities.

OBJECTIVE: Hospitalized patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for adverse outcomes. Yet, absence of effective prognostic tools hinders optimal care planning and decision making. Our objective was to develop and validate a risk prediction model for 6-month all-cause death among hospitalized patients discharged to SNFs. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: Patients discharged from 1 of 2 hospitals to 1 of 10 SNFs for post-acute care in an integrated health care delivery system between January 1, 2009, and December 31, 2016. METHODS: Gradient-boosting machine modeling was used to predict all-cause death within 180 days of hospital discharge with use of patient demographic characteristics, comorbidities, pattern of prior health care use, and clinical parameters from the index hospitalization. Area under the receiver operating characteristic curve (AUC) was assessed for out-of-sample observations under 10-fold cross-validation. RESULTS: We identified 9803 unique patients with 11,647 hospital-to-SNF discharges [mean (SD) age, 80.72 (9.71) years; female sex, 61.4%]. These discharges involved 9803 patients alive at 180 days and 1844 patients who died between day 1 and day 180 of discharge. Age, comorbid burden, health care use in prior 6 months, abnormal laboratory parameters, and mobility status during hospital stay were the most important predictors of 6-month death (model AUC, 0.82). CONCLUSION AND IMPLICATIONS: We derived a robust prediction model with parameters available at discharge to SNFs to calculate risk of death within 6 months. This work may be useful to guide other clinicians wishing to develop mortality prediction instruments specific to their post-acute SNF populations.

Real-World Incidence of Breakthrough Coronavirus Disease 2019 Hospitalization After Vaccination vs Natural Infection in a Large, Local, Empaneled Primary Care Population Using Time-to-Event Analysis.

We followed 106 349 primary care patients for 22 385 3099 person-days across 21 calendar months and documented 69 breakthrough coronavirus disease 2019 (COVID-19) hospitalizations: 65/102,613 (0.06%) among those fully vaccinated, 3/11 047 (0.03%) among those previously infected, and 1/7,313 (0.01%) among those with both statuses. These data give providers real-world context regarding breakthrough COVID-19 hospitalization risk.

Improving time to palliative care review with predictive modeling in an inpatient adult population: study protocol for a stepped-wedge, pragmatic randomized controlled trial.

BACKGROUND: Palliative care is a medical specialty centered on improving the quality of life (QOL) of patients with complex or life-threatening illnesses. The need for palliative care is increasing and with that the rigorous testing of triage tools that can be used quickly and reliably to identify patients that may benefit from palliative care. METHODS: To that aim, we will conduct a two-armed stepped-wedge cluster randomized trial rolled out to two inpatient hospitals to evaluate whether a machine learning algorithm accurately identifies patients who may benefit from a comprehensive review by a palliative care specialist and decreases time to receiving a palliative care consult in hospital. This is a single-center study which will be conducted from August 2019 to November 2020 at Saint Mary's Hospital & Methodist Hospital both within Mayo Clinic Rochester in Minnesota. Clusters will be nursing units which will be chosen to be a mix of complex patients from Cardiology, Critical Care, and Oncology and had previously established relationships with palliative medicine. The stepped wedge design will have 12 units allocated to a design matrix of 5 treatment wedges. Each wedge will last 75 days resulting in a study period of 12 months of recruitment unless otherwise specified. Data will be analyzed with Bayesian hierarchical models with credible intervals denoting statistical significance. DISCUSSION: This intervention offers a pragmatic approach to delivering specialty palliative care to hospital patients in need using machine learning, thereby leading to high value care and improved outcomes. It is not enough for AI to be utilized by simply publishing research showing predictive performance; clinical trials demonstrating better outcomes are critically needed. Furthermore, the deployment of an AI algorithm is a complex process that requires multiple teams with varying skill sets. To evaluate a deployed AI, a pragmatic clinical trial can accommodate the difficulties of clinical practice while retaining scientific rigor. TRIAL REGISTRATION: ClinicalTrials.gov NCT03976297 . Registered on 6 June 2019, prior to trial start.

Early Detection of Post-Surgical Complications using Time-series Electronic Health Records.

Models predicting health complications are increasingly attempting to reflect the temporally changing nature of patient status. However, both the practice of medicine and electronic health records (EHR) have yet to provide a true longitudinal representation of a patient's medical history as relevant data is often asynchronous and highly missing. To match the stringent requirements of many static time models, time-series data has to be truncated, and missing values in samples have to be filled heuristically. However, these data preprocessing procedures may unconsciously misinterpret real-world data, and eventually lead into failure in practice. In this work, we proposed an augmented gated recurrent unit (GRU), which formulate both missingness and timeline signals into GRU cells. Real patient data of post-operative bleeding (POB) after Colon and Rectal Surgery (CRS) was collected from Mayo Clinic EHR system to evaluate the effectiveness of proposed model. Conventional models were also trained with imputed dataset, in which event missingness or asynchronicity were approximated. The performance of proposed model surpassed current state-of-the-art methods in this POB detection task, indicating our model could be more eligible to handle EHR datasets.

Quantifying the Importance of COVID-19 Vaccination to Our Future Outlook.

Predictive models have played a critical role in local, national, and international response to the COVID-19 pandemic. In the United States, health care systems and governmental agencies have relied on several models, such as the Institute for Health Metrics and Evaluation, Youyang Gu (YYG), Massachusetts Institute of Technology, and Centers for Disease Control and Prevention ensemble, to predict short- and long-term trends in disease activity. The Mayo Clinic Bayesian SIR model, recently made publicly available, has informed Mayo Clinic practice leadership at all sites across the United States and has been shared with Minnesota governmental leadership to help inform critical decisions during the past year. One key to the accuracy of the Mayo Clinic model is its ability to adapt to the constantly changing dynamics of the pandemic and uncertainties of human behavior, such as changes in the rate of contact among the population over time and by geographic location and now new virus variants. The Mayo Clinic model can also be used to forecast COVID-19 trends in different hypothetical worlds in which no vaccine is available, vaccinations are no longer being accepted from this point forward, and 75% of the population is already vaccinated. Surveys indicate that half of American adults are hesitant to receive a COVID-19 vaccine, and lack of understanding of the benefits of vaccination is an important barrier to use. The focus of this paper is to illustrate the stark contrast between these 3 scenarios and to demonstrate, mathematically, the benefit of high vaccine uptake on the future course of the pandemic.

Deployment of an Interdisciplinary Predictive Analytics Task Force to Inform Hospital Operational Decision-Making During the COVID-19 Pandemic.

In March 2020, our institution developed an interdisciplinary predictive analytics task force to provide coronavirus disease 2019 (COVID-19) hospital census forecasting to help clinical leaders understand the potential impacts on hospital operations. As the situation unfolded into a pandemic, our task force provided predictive insights through a structured set of visualizations and key messages that have helped the practice to anticipate and react to changing operational needs and opportunities. The framework shared here for the deployment of a COVID-19 predictive analytics task force could be adapted for effective implementation at other institutions to provide evidence-based messaging for operational decision-making. For hospitals without such a structure, immediate consideration may be warranted in light of the devastating COVID-19 third-wave which has arrived for winter 2020-2021.

Improving the delivery of palliative care through predictive modeling and healthcare informatics.

OBJECTIVE: Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team. MATERIALS AND METHODS: Electronic health record data for 68 349 inpatient encounters in 2017 at a large hospital were used to train a model to predict the need for PC consult. This model was published as a web service, connected to institutional data pipelines, and consumed by a downstream display application monitored by the PC team. For those patients that the PC team deems appropriate, a team member then contacts the patient's corresponding care team. RESULTS: Training performance AUC based on a 20% holdout validation set was 0.90. The most influential variables were previous palliative care, hospital unit, Albumin, Troponin, and metastatic cancer. The model has been successfully integrated into the clinical workflow making real-time predictions on hundreds of patients per day. The model had an "in-production" AUC of 0.91. A clinical trial is currently underway to assess the effect on clinical outcomes. CONCLUSIONS: A machine learning model can effectively predict the need for an inpatient PC consult and has been successfully integrated into practice to refer new patients to PC.

Anemia in Critically Ill Patients With Acute Respiratory Distress Syndrome and Posthospitalization Physical Outcomes.

OBJECTIVE: Anemia is common during critical illness and often persists after hospital discharge; however, its potential association with physical outcomes after critical illness is unclear. Our objective was to assess the associations between hemoglobin at intensive care unit (ICU) and hospital discharge with physical status at 3-month follow-up in acute respiratory distress syndrome (ARDS) survivors. METHODS: This is a secondary analysis of a multisite prospective cohort study of 195 mechanically ventilated ARDS survivors from 13 ICUs at 4 teaching hospitals in Baltimore, Maryland. Multivariable regression was utilized to assess the relationships between ICU and hospital discharge hemoglobin concentrations with measures of physical status at 3 months, including muscle strength (Medical Research Council sumscore), exercise capacity (6-minute walk distance [6MWD]), and self-reported physical functioning (36-Item Short-Form Health Survey [SF-36v2] Physical Function score and Activities of Daily Living [ADL] dependencies). RESULTS: Median (interquartile range) hemoglobin concentrations at ICU and hospital discharge were 9.5 (8.5-10.7) and 10.0 (9.0-11.2) g/dL, respectively. In multivariable regression analyses, higher ICU discharge hemoglobin concentrations (per 1 g/dL) were associated with greater 3-month 6MWD mean percent of predicted (3.7% [95% confidence interval 0.8%-6.5%]; P = .01) and fewer ADL dependencies (-0.2 [-0.4 to -0.1]; P = .02), but not with percentage of maximal muscle strength (0.7% [-0.9 to 2.3]; P = .37) or SF-36v2 normalized Physical Function scores (0.8 [-0.3 to 1.9]; P = .15). The associations of physical outcomes and hospital discharge hemoglobin concentrations were qualitatively similar, but none were statistically significant. CONCLUSIONS: In ARDS survivors, higher hemoglobin concentrations at ICU discharge, but not hospital discharge, were significantly associated with improved exercise capacity and fewer ADL dependencies. Future studies are warranted to further assess these relationships.

Use of a Computerized Algorithm to Evaluate the Proportion and Causes of Potentially Preventable Readmissions Among Patients Discharged to Skilled Nursing Facilities.

OBJECTIVES: Older patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for hospital readmission. Yet, as in the community setting, some readmissions may be preventable with optimal transitional care. This study examined the proportion of 30-day hospital readmissions from SNFs that could be considered potentially preventable readmissions (PPRs) and evaluated the reasons for these readmissions. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: Post-acute practice of an integrated health care delivery system serving 11 SNFs in the US Midwest. Patients discharged from the hospital to an SNF and subsequently readmitted to the hospital within 30 days from January 1, 2009, through November 31, 2016. METHODS: A computerized algorithm evaluated the relationship between initial and repeat hospitalizations to determine whether the repeat hospitalization was a PPR. We assessed for changes in PPR rates across the system over the study period and evaluated the readmission categories to identify the most prevalent PPR categories. RESULTS: Of 11,976 discharges to SNFs for post-acute care among 8041 patients over the study period, 16.6% resulted in rehospitalization within 30 days, and 64.8% of these rehospitalizations were considered PPRs. Annual proportion of PPRs ranged from 58.2% to 66.4% [mean (standard deviation) 0.65 (0.03); 95% confidence interval CI 0.63-0.67; P = .36], with no discernable trend. Nearly one-half (46.2%) of all 30-day readmissions were classified as potentially preventable medical readmissions related to recurrence or continuation of the reason for initial admission or to complications from the initial hospitalization. CONCLUSIONS AND IMPLICATIONS: For this cohort of patients discharged to SNFs, a computerized algorithm categorized a large proportion of 30-day hospital readmissions as potentially preventable, with nearly one-half of those linked to the reason for the initial hospitalization. These findings indicate the importance of improvement in postdischarge transitional care for patients discharged to SNFs.

Challenges of Modeling Outcomes for Surgical Infections: A Word of Caution.

Background: We developed a novel analytic tool for colorectal deep organ/space surgical site infections (C-OSI) prediction utilizing both institutional and extra-institutional American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) data. Methods: Elective colorectal resections (2006-2014) were included. The primary end point was C-OSI rate. A Bayesian-Probit regression model with multiple imputation (BPMI) via Dirichlet process handled missing data. The baseline model for comparison was a multivariable logistic regression model (generalized linear model; GLM) with indicator parameters for missing data and stepwise variable selection. Out-of-sample performance was evaluated with receiver operating characteristic (ROC) analysis of 10-fold cross-validated samples. Results: Among 2,376 resections, C-OSI rate was 4.6% (n = 108). The BPMI model identified (n = 57; 56% sensitivity) of these patients, when set at a threshold leading to 80% specificity (approximately a 20% false alarm rate). The BPMI model produced an area under the curve (AUC) = 0.78 via 10-fold cross- validation demonstrating high predictive accuracy. In contrast, the traditional GLM approach produced an AUC = 0.71 and a corresponding sensitivity of 0.47 at 80% specificity, both of which were statstically significant differences. In addition, when the model was built utilizing extra-institutional data via inclusion of all (non-Mayo Clinic) patients in ACS-NSQIP, C-OSI prediction was less accurate with AUC = 0.74 and sensitivity of 0.47 (i.e., a 19% relative performance decrease) when applied to patients at our institution. Conclusions: Although the statistical methodology associated with the BPMI model provides advantages over conventional handling of missing data, the tool should be built with data specific to the individual institution to optimize performance.

Correlations Between COVID-19 Cases and Google Trends Data in the United States: A State-by-State Analysis.

OBJECTIVE: To evaluate whether a digital surveillance model using Google Trends is feasible for obtaining accurate data on coronavirus disease 2019 and whether accurate predictions can be made regarding new cases. METHODS: Data on total and daily new cases in each US state were collected from January 22, 2020, to April 6, 2020. Information regarding 10 keywords was collected from Google Trends, and correlation analyses were performed for individual states as well as for the United States overall. RESULTS: Among the 10 keywords analyzed from Google Trends, face mask, Lysol, and COVID stimulus check had the strongest correlations when looking at the United States as a whole, with R values of 0.88, 0.82, and 0.79, respectively. Lag and lead Pearson correlations were assessed for every state and all 10 keywords from 16 days before the first case in each state to 16 days after the first case. Strong correlations were seen up to 16 days prior to the first reported cases in some states. CONCLUSION: This study documents the feasibility of syndromic surveillance of internet search terms to monitor new infectious diseases such as coronavirus disease 2019. This information could enable better preparation and planning of health care systems.

Prevalence of and Recovery From Anemia Following Hospitalization for Critical Illness Among Adults.

Importance: Anemia is common and has been associated with poor outcomes in the critically ill population, yet the timing and extent of hemoglobin recovery remains incompletely described, which may have important implications for clinical outcomes following discharge from intensive care. Objectives: To describe longitudinal changes in anemia status during and after critical illness and assess the associations between hemoglobin concentrations and postdischarge mortality. Design, Setting, and Participants: A population-based cohort study was conducted from January 1, 2010, to December 31, 2016, in Olmsted County, Minnesota; data analysis was performed from June 1 to December 30, 2019. Participants included 6901 adults (age ≥18 years) admitted to intensive care. Main Outcomes and Measures: Hemoglobin concentrations in the 12 months before hospitalization, during hospitalization, and in the 12 months after discharge, categorized by anemia severity (mild, hemoglobin ≥10.0 to <12.0 g/dL in women or ≥10.0 to <13.5 g/dL in men; moderate, hemoglobin ≥8.0 to <10.0 g/dL; and severe, hemoglobin <8.0 g/dL). Complete recovery from anemia, defined as attainment of nonanemic status by 12 months post hospitalization, and 12-month mortality were also evaluated. Results: Of the 6901 patients included in the study, 3792 were men (55%); median (interquartile range [IQR]) age was 67 (IQR, 52-79) years. Prehospitalization hemoglobin concentrations were available in 83% of the population (n = 5694), with median hemoglobin concentrations of 13.1 (IQR, 11.6-14.4) g/dL. Forty-one percent of the patients (n = 2320) had anemia preceding hospitalization. Hemoglobin values at hospital discharge were 10.8 g/dL (IQR, 9.5-12.4 g/dL), with 80% (n = 5182 of 6460) having anemia: 58% mild, 39% moderate, and 3% severe. The prevalence of anemia post hospitalization was 56% (95% CI, 55%-58%) at 3 months, 52% (95% CI, 50%-54%) at 6 months, and 45% (95% CI, 43%-47%) at 12 months among those alive with available hemoglobin measurements. Rates of complete recovery from anemia at 12 months were 58% (95% CI, 56%-61%) for mild anemia, 39% (95% CI, 36%-42%) for moderate anemia, and 24% (95% CI, 15%-34%) for severe anemia. Of those without baseline anemia surviving hospitalization, 74% of the patients were anemic at hospital discharge, with rates of complete 12-month recovery of 73% (95% CI, 69%-76%) for mild anemia, 62% (95% CI, 57%-68%) for moderate anemia, and 59% (95% CI, 35%-82%) for severe anemia. Higher hospital discharge hemoglobin concentrations were associated with decreased mortality after multivariable adjustment (hazard ratio, 0.95 per 1-g/dL increase; 95% CI, 0.90-0.99, P = .02). Conclusions and Relevance: The findings of this study suggest that anemia is common and often persistent in the first year after critical illness. Further studies are warranted to identify distinct anemia recovery profiles and assess associations with clinical outcomes.

Pragmatic cluster randomized trial to evaluate effectiveness and implementation of enhanced EHR-facilitated cancer symptom control (E2C2).

BACKGROUND: The prevalence of inadequate symptom control among cancer patients is quite high despite the availability of definitive care guidelines and accurate and efficient assessment tools. METHODS: We will conduct a hybrid type 2 stepped wedge pragmatic cluster randomized clinical trial to evaluate a guideline-informed enhanced, electronic health record (EHR)-facilitated cancer symptom control (E2C2) care model. Teams of clinicians at five hospitals that care for patients with various cancers will be randomly assigned in steps to the E2C2 intervention. The E2C2 intervention will have two levels of care: level 1 will offer low-touch, automated self-management support for patients reporting moderate sleep disturbance, pain, anxiety, depression, and energy deficit symptoms or limitations in physical function (or both). Level 2 will offer nurse-managed collaborative care for patients reporting more intense (severe) symptoms or functional limitations (or both). By surveying and interviewing clinical staff, we will also evaluate whether the use of a multifaceted, evidence-based implementation strategy to support adoption and use of the E2C2 technologies improves patient and clinical outcomes. Finally, we will conduct a mixed methods evaluation to identify disparities in the adoption and implementation of the E2C2 intervention among elderly and rural-dwelling patients with cancer. DISCUSSION: The E2C2 intervention offers a pragmatic, scalable approach to delivering guideline-based symptom and function management for cancer patients. Since discrete EHR-imbedded algorithms drive defining aspects of the intervention, the approach can be efficiently disseminated and updated by specifying and modifying these centralized EHR algorithms. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03892967. Registered on 25 March 2019.

Modeling material stress using integrated Gaussian Markov random fields.

The equations of a physical constitutive model for material stress within tantalum grains were solved numerically using a tetrahedrally meshed volume. The resulting output included a scalar vonMises stress for each of the more than 94,000 tetrahedra within the finite element discretization. In this paper, we define an intricate statistical model for the spatial field of vonMises stress which uses the given grain geometry in a fundamental way. Our model relates the three-dimensional field to integrals of latent stochastic processes defined on the vertices of the one- and two-dimensional grain boundaries. An intuitive neighborhood structure of the said boundary nodes suggested the use of a latent Gaussian Markov random field (GMRF). However, despite the potential for computational gains afforded by GMRFs, the integral nature of our model and the sheer number of data points pose substantial challenges for a full Bayesian analysis. To overcome these problems and encourage efficient exploration of the posterior distribution, a number of techniques are now combined: parallel computing, sparse matrix methods, and a modification of a block update strategy within the sampling routine. In addition, we use an auxiliary variables approach to accommodate the presence of outliers in the data.